Experimental detection of regular dynamics using the permutation slopes

Time series are useful for modelling systems behaviour, for predicting some events (catastrophes, epidemics, weather?) or for classification purposes (pattern recognition, pattern analysis). Among the existing data analysis algorithms, ordinal pattern based algorithms have been shown effective when dealing with simulation data. However, applying these algorithms to real-world data remains a challenging task which we are addressing in our research. For this purpose, we developed the permutation largest slope entropy (PLSE) which is particularly fast and highly robust against noise, hence useful for real-time analysis of real-world data. In this talk, we show the effectiveness of the PLSE for detecting changes and classifying dynamics of the Duffing oscillator. For the generation of experimental data, we used the equivalent electronic circuit of the Duffing oscillator and a digital oscilloscope with 1MHz sampling frequency.